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Observational study
 Record data on individuals without attempting to influence the
 responses. We typically cannot prove anything this way.

Experimental study
 Deliberately impose a treatment on individuals and record their
 responses. Influential factors can be controlled.
 Observational studies of the effect of one variable on another often fail because the
 explanatory variable is confounded with lurking variables.
 Welldesigned experiments take steps to defeat confounding.

Population
The entire group of individuals in which we are interested but can’t usually assess directly.

Parameter
a number describing a characteristic of the population.

Sample
 The part of the population we actually examine and for which we do have data
 How well the sample represents the population depends on the sample design.
 Statistic is a number describing a characteristic of a sample

Bad sampling methods
Convenience sampling & bias

Convenience sampling
Just ask whoever is around.

Bias
Opinions limited to individuals present

Voluntary Response Sampling
Individuals choose to be involved

Bias
Sample design systematically favors a particular outcome.

Good sampling methods
Probability or random sampling

Probability or random sampling
 Individuals are randomly selected.
 Sampling randomly gets rid of bias.

Simple random sample
 (SRS) is made of randomly selected individuals. Each
 individual in the population has the same probability of being in the sample. All possible
 samples of size n have the same chance of being drawn.


How to choose an SRS of size n from a population of size N:
 1. Label
 2. Table B
 3.Stratified random sample

Label
Give each member of the population a numerical label of the same length.

Table B
Read from Table B successive groups of digits of the length you used as labels. Your sample contains the individuals whose labels you find in the table.

Stratified random sample
 a series of SRS performed on subgroups of a given
 population. The subgroups are chosen to contain all the individuals with a certain characteristic.
 The SRS taken within each group in a stratified random sample need not be of the same
 size.

Caution about sampling surveys
 Nonresponse
 Response bias
 Wording effects
 Undercoverage

Learning about populations from samples
 The techniques of inferential statistics allow us to draw inferences or conclusions about a
 population from a sample.
 Your estimate of the population is only as good as your sampling design Work hard to eliminate biases.
 Your sample is only an estimate—and if you randomly sampled again, you would probably get a somewhat different result.
 The bigger the sample the better.

